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Egr1 regulates regenerative senescence and cardiac repair

Nature Cardiovascular Research volume 3, pages 915–932 (2024)Cite this article

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Abstract

Senescence plays a key role in various physiological and pathological processes. We reported that injury-induced transient senescence correlates with heart regeneration, yet the multi-omics profile and molecular underpinnings of regenerative senescence remain obscure. Using proteomics and single-cell RNA sequencing, here we report the regenerative senescence multi-omic signature in the adult mouse heart and establish its role in neonatal heart regeneration and agrin-mediated cardiac repair in adult mice. We identified early growth response protein 1 (Egr1) as a regulator of regenerative senescence in both models. In the neonatal heart, Egr1 facilitates angiogenesis and cardiomyocyte proliferation. In adult hearts, agrin-induced senescence and repair require Egr1, activated by the integrin–FAK–ERK–Akt1 axis in cardiac fibroblasts. We also identified cathepsins as injury-induced senescence-associated secretory phenotype components that promote extracellular matrix degradation and potentially assist in reducing fibrosis. Altogether, we uncovered the molecular signature and functional benefits of regenerative senescence during heart regeneration, with Egr1 orchestrating the process.

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Fig. 1: The protein signatures in two mouse models of cardiac regeneration reveal enriched senescence gene sets.
Fig. 2: Egr1 is required for regenerative senescence and agrin-mediated cardiac repair in adult mice.
Fig. 3: Single-cell RNA sequencing defines the senescence signature of cardiac fibroblasts.
Fig. 4: Egr1 regulates cardiac cell fates and is required for neonatal mouse heart regeneration.
Fig. 5: Egr1 expression in CFs is regulated by integrin–FAK–ERK1/2–Akt1 signaling.
Fig. 6: Cathepsin S exhibits high expression levels in both senescent mouse models and induces ECM remodeling.
Fig. 7: An illustration of the role of Egr1 in two mouse models of cardiac injury.

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Data availability

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE59 partner repository with the dataset identifier PXD048877. The scRNA-seq data supporting the findings of this study has been archived at Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) with the accession number GSE255181. 10x SpaceRanger processed the Visium raw data in the Gene Expression Omnibus with the accession number GSE253383. All data supporting the findings in this study are included in the main article and Supplementary Information. Source data describing the values used in quantifications mentioned in the text and figures are provided with this paper.

Code availability

The scripts utilized for analyzing the scRNA-seq dataset and quantifying the fibrotic area in heart sections are available on GitHub at https://github.com/wtzllzhang/Script-files. Additionally, the code used to analyze the Visium dataset is available at GitHub at https://github.com/lengfei5/ST_visium_mice.

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Acknowledgements

This work was supported by grants to E.T. from the European Research Council (ERC AdG grant no. 788194, CardHeal), ERA-CVD CARDIO-PRO, the Israel Science Foundation (ISF, grant no. 2214/22), the Yotam project and the Weizmann institute sustainability and energy research initiative (grant no. 142735), the Minerva Center on ‘Aging, from physical materials to human tissues’, the Israel Ministry of Science and Technology, the EU Horizon 2020 research innovation programme REANIMA (providing grants to E.T. and E.M.T., grant no. 874764), the Estate of Caroline Cancelmo, the Erica Drake Fund, the Weizmann UK Building for Biocomplexity Research (GG 2016), the Aharon and Tova (Buena) Cohen Memorial Fund, the Horwitz Research Fund, the Yacov Chaoul Kapczuk Fund, the Estate of Elizabeth Wachsman, Seed for Peace. Inc., the Jacques Asseoff Trust, the Gurwin Family Fund for Scientific Research and the Midwest Electron Microscope Project. We thank T. Eigler-Hirsh, S. Cheng, A. Aharonov, W. Li, G. Aviel, Y. Divinsky, Z. Petrover, E. Amzallag, E. Spector, L. Roitman, A. Agrawal, J.-M. Majewska, Z. Porat and N. Halevi for their suggestions. We thank M. Cohen from the Histology Unit, Department of Veterinary Resources, Weizmann Institute of Science, for histological processing and staining. We thank T. Moshe and S. Ulman for animal husbandry. We thank R. Ronen and R. Blecher from the Crown Genomics Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine for single-cell sample processing (library preparation and data sequencing). We thank M. Yehudi, H. David, G. Attias, S. Vishner and Z. Dobrish for technical laboratory assistance. Illustrations in Fig. 7 were created with BioRender.

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Author notes

  1. These authors contributed equally: Rachel Sarig, Eldad Tzahor.

Authors and Affiliations

  1. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Lingling Zhang, Jacob Elkahal, Racheli Rimmer, Alexander Genzelinakh, Dahlia Perez, David Kain, Daria Lendengolts, Roni Winkler, Hanna Bueno-levy, Kfir Baruch Umansky, Avraham Shakked, Shoval Miyara, Naomi Goldfinger, Varda Rotter, Valery Krizhanovsky, Rachel Sarig & Eldad Tzahor

  2. Department of Bimolecular Sciences, Weizmann Institute of Science, Rehovot, Israel

    Tianzhen Wang

  3. Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria

    Elad Bassat, Jingkui Wang & Elly M. Tanaka

  4. Veterinary Resource, Weizmann Institute of Science, Rehovot, Israel

    Hanna Bueno-levy

  5. Pediatric Heart Institute, Edmond and Lily Safra Children’s Hospital, Sheba Medical Center, Tel Hashomer, Israel

    David Mishaly & Uriel Katz

  6. The Mantoux Bioinformatics Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel

    Avital Sarusi-Portuguez

  7. The de Botton Institute for Protein Profiling of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel

    Amir Prior, David Morgenstern & Yishai Levin

  8. Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel

    Yoseph Addadi

  9. Department of System Immunology, Weizmann Institute of Science, Rehovot, Israel

    Baoguo Li

  10. Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel

    Uriel Katz

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  1. Lingling Zhang
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  2. Jacob Elkahal
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Contributions

Conceptualization was the responsibility of L.L.Z., R.S. and E.T. Data analysis was the responsibility of L.L.Z., T.Z.W., A.S.P., A.G., R.S., D.P., J.K.W., A.P., D.M. and Y.L. Experimental work was the responsibility of L.L.Z., R.S., T.Z.W., J.E., R.R., A.G., E.B., D.K., D.L., R.W., H.B.L., K.B.U., A.S., S.M., N.G., B.L. and Y.A. Advice, guidance and discussion of results and strategy was the responsibility of V.R., U.K., D.M., E.M.T., V.K. and U.K. L.L.Z., R.S. and E.T. wrote the manuscript. All authors approved the final version of the paper.

Corresponding authors

Correspondence to Rachel Sarig or Eldad Tzahor.

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Competing interests

E.T. is a founder and advisor for a biomedical company specializing in advancing agrin therapy for heart diseases. K.B.U. is a co-founder and employee of the same biomedical company. All other authors declare no competing interests.

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Nature Cardiovascular Research thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended Data

Extended Data Fig. 1 Senescence-related pathways are enriched in Agrin-treated adult hearts and injured neonatal hearts.

a, Heatmap generated from proteomics data illustrating proteins linked to senescence/injury-related pathways. b, Volcano plots illustrated DE proteins in Agrin_sen vs PBS and AR_sen vs Uninjured comparasions, with a significance threshold of p < 0.05 and |log2FC| > 0.6. Protein intensities, FC, and P values are provided in Supplementary Table 1. c, Clusters of differentially enriched gene sets from the Gene Ontology(GO) module(GSEA) in Agrin_sen vs PBS groups are presented. Gene sets with p-value < 0.05 and FDR < 0.25 were considered shared if their Jaccard coefficient exceeded 0.25. Node size corresponds to the number of genes identified in the gene set, and edges represent overlapping genes between gene sets. d, The Venn diagram illustrated shared DE proteins in Agrin_sen and AR_sen(upper panel), and common DE proteins in Agrin_sen, ionizing radiation(IR) and RAS-induced senescence in primary human lung fibroblasts(lower panel). Details are provided in Supplementary Table 2. DE proteins from IR or RAS-induced senescence were sourced from Nathan Basisty et al. (The proteomic profiles can be accessed by the following link: 10.1371/journal.pbio.3000599.s007).

Source data

Extended Data Fig. 2 Senescence-related molecules are elevated in injured neonatal hearts and enriched cardiac fibroblast cultures.

a-e, Immunoblot analysis of p53 (a), p21 (b), p16 (c), p65 (d), and IкBα (e) levels in uninjured P5 apex and AR_sen samples. n = 5 hearts for Uninjured and n = 4 hearts for AR_sen. Graphs at the bottom show the Quantification. Data are presented as mean ± s.e.m. Statistical significance was calculated using Two-tailed unpaired t-test in (a-e). The threshold for statistical significance was considered as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. f, Representative IF images of CF marker(DDR2, Col3a1, and Vimentin), myofibroblast marker(α-SMA), CM marker(cTnT), and endothelial cell marker(CD31), and immune cell marker(CD45) in enriched CF cultures derived from P1 and adult mouse hearts. Scale bars represent 20 μm. g,h, Real-time PCR analysis of distinct cellular markers in enriched CF cultures observed in (f). n = 3 biological replicates. Data are presented as mean ± s.e.m. i, Co-staining of SA−β-Gal with Vimentin, DDR2, or α-SMA was conduced in enriched P1 and adult CF cultures. The arrows point to cells that are SA-β-Gal+ Vimentin+, SA-β-Gal+ DDR2+, or SA-β-Gal+ α-SMA-. Scale bars represent 20 μm. j, Quantification of Vimentin, DDR2, and α-SMA in SA-β-Gal+ cells observed in (i). n = 3 biological replicates. Data are presented as mean ± s.e.m. k, Co-staining of SA-β-Gal with p53, p21, p16, and p27 was conducted in enriched P1 CF cultures. The arrows point to the cells expressing both markers. Scale bars represent 20 μm. l,m, Quantification of the percentage of cells expressing p53, p21, p16, and p27 in SA-β-Gal+ cells in enriched P1 (l) and adult (m) CF cultures. n = 3 biological replicates. Data are presented as mean ± s.e.m.

Source data

Extended Data Fig. 3 Agrin increased the expression of senescence-related molecules in Egr1 WT hearts but not KO hearts.

a, Immunoblot analysis of Egr1, p53, p21, and p16 levels in PBS or Agrin-treated Egr1 WT and KO mouse hearts 4 days PMI. b, The Quantification of protein expression levels observed in (a). n = 3 hearts in all groups. c, Representative IF images of CF marker(DDR2), p53, and cTnT in PBS or Agrin-treated Egr1 WT and KO mouse hearts 4 days PMI. Scale bars represent 10 μm. d, The Quantification of co-staining for p53 and DDR2 observed in (c). n = 4 hearts in WT_PBS, WT_Agrin, and KO_Agrin groups. n = 3 hearts in KO_PBS group. e, Co-staining of SA-β-Gal with p53 was conducted in enriched CF cultures derived from P1 and adult mouse hearts. The arrows indicate cells positive for SA-β-Gal and p53. n = 3 biological replicates. Scale bars represent 10 μm. f, Immunoblot analysis of Agrin levels in Egr1 WT and KO hearts derived from uninjured P1 and adult mouse hearts(up panel). n = 3 for P1 hearts, n = 4 for adult hearts. Immunoblot analysis of Agrin in cardiac cultures derived from P1 and adult Egr1 WT and KO mouse hearts (low panel). n = 3 biological replicates in all groups. The Quantification of full-length Agrin levels is shown on the right. g, Real-time PCR analysis of Agrin in adult Egr1 WT and KO mouse hearts 4 days PMI. n = 4 for Egr1 WT hearts, n = 3 for Egr1 KO hearts. Data are presented as mean ± s.e.m. Statistical significance was calculated using Two-tailed unpaired t-test in (b_left, only for Egr1, g), and Two-way ANOVA with Tukey’s multiple comparisons test in (b_p53, p21 and p16, d, f). The threshold for statistical significance was considered as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, and ns, not significant.

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Extended Data Fig. 4 Performing scRNA-seq on mouse hearts following MI.

a, Schematic depicting single cell isolation through gentleMACS, debris removal, red blood cell(RBC) lysis, followed by enrichment for macrophages and endothelial cells using CD45 and CD31 microbeads. 75% of single cells were collected from CD45 and CD31-depleted cells, and 25% were obtained from CD45 and CD31-enriched cells for sequencing. Illustrations were created with BioRender. b, Violin plots demonstrated that cells were filtered based on 4 Median Absolute Deviation(MAD) below and above the median for each parameter and sample (as delineated in the graph). Black dots represent the median, and the lines indicate 4 MADs above and below the median. nFeature, number of genes per cell; nCount, number of UMIs(Unique Molecular Identifiers) per cell; percent.mt, percentage of mitochondrial reads per cell. n = 3 hearts in all groups. c. Dotplot depicting the classic marker genes used to identify clusters. Dot sizes denote the percentage of expression(pct exp) per cluster; color gradient defines average expression(ave exp) per cluster. The comprehensive marker list for each cluster is available in Supplementary Table 3. d, The percentage of cell numbers in each cluster across all groups, relative to the total number of cells across all groups, was calculated. e, The percentage of cell numbers in each cluster within each group, relative to the total number of cells in each group, was calculated.

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Extended Data Fig. 5 A unique single-cell RNA sequencing defines the senescence signature of cardiac fibroblasts.

a, The enrichment score was computed using two gene lists (Supplementary Table 4) and visualized on a UMAP. Scores are represented by color, spanning from low to high. b, The enrichment score for identifying senescent cells was computed using Gene list_2. The cutoff for the enrichment score is set at 0.2. c, Cells were categorized as Sen cells if their enrichment score exceeded 0.3 and as Non-sen cells if their score fell below 0.3. d, 5,129 Sen cells were shared between Gene list_1 and Gene list _2. 942 Sen cells were exclusively identified by Gene list_1, and 5,184 Sen cells were uniquely associated with Gene list_2. Two-sided Pearson’s correlation coefficient method was used to determine linear correlation. R = 0.89. e, Dotplot illustrating marker genes to identify clusters. Dot sizes denote the percentage of expression(pct exp) per cluster; color gradient defines average expression(ave exp) per cluster. The comprehensive marker list for each cluster is available in Supplementary Table 5. f, The percentage of Sen cell numbers in each cluster within WT_PBS or WT_Agrin group, relative to the total number of Sen cells in WT_PBS or WT_Agrin group, was calculated. n = 3 hearts in all groups. Data are presented as mean ± s.e.m. Statistical significance was calculated using Two-tailed unpaired t-test. g, The percentage of Sen cells in each cluster within WT_PBS or WT_Agrin group, relative to the total number of cells in each cluster within WT_PBS or WT_Agrin group, was calculated. h, IPA was conducted on DE genes between Sen and Non-sen cells in cluster 0 in WT_Agrin and WT_PBS groups. This analysis revealed unique pathways between the two groups. i, Clusters of differentially enriched gene sets from GO in Sen and Non-sen groups were displayed. Gene sets with p-value < 0.05 and FDR < 0.25 were considered shared if their Jaccard coefficient exceeded 0.25. The size of nodes corresponds to the number of genes identified in the gene sets, and edges represent overlap between gene sets. j, Violin plots depicted genes related to the gene sets shown in (i) and Fig. 3l in Sen and Non-sen cells.

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Extended Data Fig. 6 Senescence is crucial for neonatal heart regeneration.

a, Representative SA-β-Gal staining images in enriched CF cultures derived from P1 mouse hearts. Cultures were treated with DMSO, Adezmapimod(10 μM), the combination of Dasatinib(200 nM) and Quercertin (20 μM) for 3 days. Scale bars represent 20 μm. b, Percentage of SA-β-Gal+ cells observed in (a). n = 2 biological replicates. c, Representative images display whole-mount SA-β-Gal staining in neonatal mouse hearts 4 days after cryoinjury. Scale bars indicate 300 μm. Injections of DMSO(20 μl), Adezmapimod(50 mg/kg, 20 μl), and a combination of Dasatinib(5 mg/kg) and Quercetin(D + Q, 50 mg/kg) (20 μl in total) were administered on days 1, 2, and 3 post-cryoinjury. d, Quantification of the stained area observed in (c) was performed. n = 8 for DMSO-treated hearts, n = 9 for Adezmapimod-treated hearts, and n = 7 for D + Q-treated hearts. e, Representative histology images of hearts stained with Sirus Red 31 days after cryoinjury. Injections of DMSO(20 μl), Adezmapimod(50 mg/kg, 20 μl), and a combination of Dasatinib(5 mg/kg) and Quercetin(50 mg/kg) were administered on days 1, 2, and 3 post-cryoinjury. Scar bars represent 100 μm. f, Scar assessment of cryoinjured hearts observed in (e). n = 4 for DMSO-treated hearts, n = 7 for Adezmapimod-treated hearts, and n = 8 for D + Q-treated hearts. Data are presented as mean ± s.e.m. Statistical significance was calculated using One-way ANOVA with Dunnett’s multiple comparisons test in (d, f). The threshold for statistical significance was considered as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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Extended Data Fig. 7 The role of Egr1 in the neonatal heart.

a, Representative images of SA-β-Gal staining in enriched CF cultures. Scale bars represent 20 μm. b, Quantification of the percentage of SA-β-Gal+ cells observed in (a). n = 3 biological replicates. c,d, Immunoblot analysis of p53 (c) and p16 (d) levels in enriched CF cultures. n = 3 biological replicates. e, Quantification of co-staining for p53 and DDR2 in Egr1_WT hearts(n = 4) and Egr1_KO hearts(n = 3). f, Representative IF images of Egr1, cTnT, and Vimentin in neonatal hearts. Scale bars represent 30 μm. g, The spatial expression of Egr1 in the cryoinjured neonatal heart was revealed through Visium analysis. The image on the left displayed H&E staining. Only one sample was utilized for Visium analysis. Scale bars represent 200 μm. h, Representative IF images of Egr1, cTnT, and Vimentin. Scale bars represent 10 μm. i, Representative IF images of Egr1, CD31, and Vimentin in cardiac cultures. 465 CD31+ cells and 2,758 Vimentin+ cells were quantified in P1 cultures isolated from P1 hearts(n = 3); 598 CD31+ cells, and 2,543 Vimentin+ cells were quantified in adult cultures isolated from adult hearts(n = 3). The average of 3 biological repeats was used for the graph. Scale bars represent 10 μm. j, Representative IF images of Ki67 and cTnT in P1 cardiac cultures. The arrows point to Ki67+ CMs. Scale bars represent 10 μm. k, IF analysis of the percentage of Ki67+ CMs in (j). 8,595 CMs were quantified in Egr1_WT hearts(n = 5); 7,179 CMs in Egr1_KO hearts(n = 5). l, Representative IF images of CD31 in P1cardiac cultures. Scale bars represent 100 μm(upper) and 20 μm(lower). m, Quantification of tube formation of ECs stained by CD31 in (l), assessed by the number of junctions, nodes, and total length. n = 4 for Egr1_WT hearts, n = 3 for Egr1_KO hearts. n, Representative IF images of Ki67 and cTnT, or CD31 and cTnT in the remote region. Scale bars represent 20 μm. o, Quantification of Ki67+ CMs and the number of vessels stained by CD31 observed in (n). 15,670 CMs were quantified in WT hearts(n = 4), and 14,276 CMs in KO hearts(n = 4). Data are presented as mean ± s.e.m. Statistical significance was calculated using Two-tailed unpaired t-test in (b-e, k, m, o). The threshold for statistical significance was considered as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, and ns, not significant.

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Extended Data Fig. 8 The staining of EGR1 in cardiac biopsies of patient.

a, Representative IF images displaying Egr1, cTnT, and Vimentin in biopsies from patients aged 7 months, 8.5 years, and 15 years. Insets indicate the magnified areas shown at the lower panels. Scale bars represent 500 μm(upper row) and 20 μm(lower row). b, The relative EGR1 fluorescence intensity observed in (a) was determined using staining from the entire section (upper panel). 3.6 mm2 and 8.8 mm2 section area were measured from 5-month-old and 7-month-old biopsies; 4.3 mm2 and 4.1 mm2 section area were measured for 8.5-year-old and 15-year-old biopsies. n = 2 infant biopies. n = 1 8.5-year-old biopsy. n = 1 15-year-old biopsy.

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Extended Data Fig. 9 Egr1 does not impact Agrin’s effects in adult mouse hearts following MI.

a, Representative IF images showing Ki67 and cTnT expression in the border zone of Egr1 WT and KO hearts treated with PBS or Agrin. The border zone was defined as 100 μm from the edge of the injured zone. Scale bars represent 20 μm(left) and 10 μm(right). b, IF analysis of the percentage of Ki67+ CMs. 5,844 CMs were quantified in WT_PBS hearts(n = 4), 6,901 CMs in WT_Agrin hearts(n = 5), 4,958 CMs in KO_PBS hearts(n = 4), and 7,839 CMs in KO_Agrin hearts(n = 6). c, Representative IF images of CD31 and cTnT in Egr1 WT and KO hearts. Yellow boxes indicate remote regions and red boxes indicate infarcted regions. Scar bars represent 500 μm(left) and 100 μm(middle and right). d, Quantification of CD31+ vessel numbers in the infarcted and remote regions of hearts from WT_PBS(n = 3), WT_Agrin(n = 3), KO_PBS(n = 5), and KO_Agrin(n = 4). e, Representative IF images of CD31 expression in cardiac cultures. Scale bars represent 30 μm. f, Tube formation quantification of ECs stained by CD31 in (e), assessed by the number of junctions, nodes, and total length. n = 3 biological replicates. g, UMAP representation of scRNA-seq data from PBS or Agrin-treated Egr1 WT and KO hearts, colored by cluster identity. n = 3 hearts for each group. h, The percentage of subclusters within immune cells was calculated in all groups. i, Volcano plot illustrated DE genes in immune cells between WT_PBS and KO_PBS groups, with a significance threshold of p < 0.05 and |log2FC| >1. Details are provided in Supplementary Table 8. j, Volcano plots illustrated DE genes in immune cells between WT_Agrin and WT_PBS groups and between KO_Agrin and KO_PBS groups, with a significance threshold of p < 0.05 and |log2FC| > 1. Details are provided in Supplementary Table 9. k, Clusters of differentially enriched gene sets from the GO (GSEA) in immune cells of hearts were compared between WT_Agrin(n = 3) and WT_PBS(n = 3), and between KO_Agrin(n = 3) and KO_PBS(n = 3). Data are presented as mean ± s.e.m. Statistical significance was calculated using Two-way ANOVA with Tukey’s multiple comparisons test in (b, d, h), and Two-tailed unpaired t-test in (f). The threshold for statistical significance was considered as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, and ns, not significant.

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Extended Data Fig. 10 Senescence of CF suppresses their proliferation.

a, Representative IF images of Ki67, cTnT, and DDR2 in PBS or Agrin-treated adult heart sections at 4 days PMI. Scale bars represent 10 μm. b, Quantification of Ki67+ DDR2+ cells in the infarcted region observed in (a). n = 24,671 cells were quantified from n = 3 PBS-treated hearts, and n = 26,612 cells were quantified from 3 Agrin-treated hearts. c, Representative IF images of Ki67, cTnT, and DDR2 in P1 Egr1 WT and KO hearts at 4 days after cryoinjury. Scale bars represent 10 μm. d, Quantification of Ki67+ DDR2+ cells in the cryoinjured region observed in (c). n = 24,424 cells were quantified from n = 4 P1 Egr1_WT, and n = 22,509 cells were quantified from n = 4 P1 Egr1_KO. e, The Visium revealed spatial expression pattern of genes associated with matrix degradation. The image on the top displayed Hematoxylin and Eosin (H&E) staining, and the injured area was marked with a yellow line. Only one sample was utilized for Visium analysis. Scale bar represents 200 μm. f, Heart sections from uninjured P5 hearts and those subjected to cryoinjury with injections of DMSO or a combination of D + Q were co-stained for SA-β-Gal with cTnT and cathepsin S. Scale bars represent 20μm. h, Quantification of Cathepsin S fluorescence intensity observed in (g). 1.94 mm2 section area was quantified across n = 6 for P5 uninjured, 1.39 mm2 section area was quantified across n = 4 for DMSO, and 1.47 mm2 section area was quantified across n = 5 for D + Q. Data are presented as mean ± s.e.m. Statistical significance was calculated using Two-tailed unpaired t-test in (b, d), One-way ANOVA with Dunnett’s multiple comparisons test in (g). The threshold for statistical significance was considered as *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

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Supplementary information

Supplementary Information

Supplementary Fig. 1 and its legend and Supplementary Protocols.

Reporting Summary

Supplementary Tables 1–11

Proteomic analysis of protein expression in agrin_sen versus PBS groups and AR_sen cells versus uninjured groups. Supplementary Table 2 DE proteins shared between agrin_sen and AR_sen groups and DE proteins common to agrin_sen, IR and RAS-induced senescence in primary human lung fibroblasts. Supplementary Table 3 The comprehensive marker list for each cluster. Supplementary Table 4 Gene list for senescence score analysis. Supplementary Table 5 The comprehensive marker list for each cluster (only FB clusters). Supplementary Table 6 scRNA-seq analysis of gene expression in Sen cells versus non-Sen cells within the WT_agrin and WT_PBS groups. Supplementary Table 7 Shared DE genes of Sen cells versus non-Sen cells within WT_agrin and WT_PBS groups. Supplementary Table 8 scRNA-seq analysis compared gene expression in immune cell clusters between the Egr1 WT_PBS and Egr1 KO_PBS groups. Supplementary Table 9 scRNA-seq analysis of gene expression in immune cells comparing Egr1 WT_agrin versus Egr1 WT_PBS and Egr1 KO_agrin versus Egr1 KO_PBS groups. Supplementary Table 10 Primers utilized for genotyping transgenic Egr1 mice, real-time PCR and integrin β1 knockdown. Supplementary Table 11 Antibodies used for WB and IF.

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Zhang, L., Elkahal, J., Wang, T. et al. Egr1 regulates regenerative senescence and cardiac repair. Nat Cardiovasc Res 3, 915–932 (2024). https://doi.org/10.1038/s44161-024-00493-1

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